1. Field of the Invention
The present invention relates to techniques for visualization of search engine output as a means for assisting the user in selecting relevant search results.
2. Description of the Related Art
The World Wide Web (“web”) contains a vast amount of information. Locating a desired portion of the information, however, can be challenging. This problem is compounded because the amount of information on the web and the number of new users inexperienced at web searching are growing rapidly.
Search engines attempt to return hyperlinks that point to web pages in which a user is interested. Generally, search engines base their determination of the user's interest on search terms (called a “search query”) entered by the user. The goal of the search engine is to provide links to high quality, relevant results to the user based on the search query. Typically, the conventional search engine accomplishes this by matching the terms in the search query to a body of pre-stored web pages. Web pages that contain the user's search terms are “hits” and are returned to the user.
In an attempt to increase the relevancy and quality of the web pages returned to the user, a search engine may attempt to sort the list of hits so that the most relevant and/or highest-quality pages are at the top of the list of hits returned to the user. For example, the search engine may assign a rank or score to each hit, where the score is designed to correspond to the relevance or importance of the web page. Determining appropriate scores can be a difficult task. For one thing, the importance of a web page to the user is inherently subjective and depends on the user's interests, knowledge, and attitudes. Conventional methods of determining relevance are based on the contents of the web page. More advanced techniques determine the importance of a web page based on more than the content of the web page, such as which pages link to it.
At present, a number of advanced system for information retrieval exist—vast sums of money are annually spend on search engine optimization, indexing more information content, document ranking, etc.
Unfortunately the rising amount of indexed databases entails one of the most common problems of standard search engines—the problem of giving too many results for a simple (from a user's point of view) query. The page ranking system tends to show the documents most frequently used in relation to the keywords, which often complicates a user's task to find a “non-typical” context of the query—the so-called “tyranny of the majority” problem. As a result, the user cannot find the required document in the first 10 to 30 results and has no simple way to specify the query. Frequently, the sought document is the 50th, or the 250th, in the search result list. Although conventional search engines continually improve the quality of annotations, the problem still exists—the user needs to think hard while reading the annotation to decide whether this document is what is sought.
To build complex queries, a query language can be used. Almost every search engine uses such a language, and with the help of logical (Boolean) operations, a user can point out the words that are to be in the required document, show how far apart they can be from each other, enumerate synonyms, and identify possible unwanted words in the required document. Unfortunately that language requires special knowledge, a change in the usual approach of most lay users, which makes the language practically inaccessible for most users, and therefore all search engines use an extra page, hidden on the main search page.
Conventional search engines also use the standard concept of a command line to type in a query. One has to type a large number of symbols that can result in a typo building the wrong query due to the wrong spelling of the required word.
The overriding goal of a search engine is to return the most desirable set of links for any particular search query. Accordingly, there is a need in the art for an effective and efficient system and method for visualizing search results as an aid to context-based searching.
Furthermore, many search concepts are not easily expressible in terms of simple Boolean operations, such as AND, OR, AND NOT, etc., but instead correspond to degrees of AND, degrees of NOT, etc. Accordingly, there is a particular need for managing searches in cases where the concepts being searched for are not easily represented by simple Boolean operators.